Electromyography Gesture Model Classifier for Fault-Tolerant-Embedded Devices by Means of Partial Least Square Class Modelling Error Correcting Output Codes (PLS-ECOC)

Abstract

Surface electromyography (sEMG) plays a crucial role in several applications, such as for prosthetic controls, human–machine interfaces (HMI), rehabilitation, and disease diagnosis. These applications are usually occurring in real-time, so the classifier tends to run on a wearable device. This edge processing paradigm imposes strict requirements on the complexity classifier. To date, research on hand gesture recognition (GR) based on sEMG uses discriminant classifiers, such as support vector machines and neural networks. These classifiers can achieve good precision; they cannot detect when an error in classification has happened. This paper proposes a novel hand gesture multiclass model based on partial least square (PLS) class modelling that uses an encoding matrix called error correcting output codes (ECOC). A dataset of eight different gestures was classified using this method where all errors were detected, proving the feasibility of PLS-ECOC as a fault-tolerant classifier. Considering the PLS-ECOC model as a classifier, its accuracy, precision, and F1 are 87.5, 91.87, and 86.34%, respectively, similar to those obtained by other authors. The strength of our work lies in the extra information provided by the PLS-ECOC that allows the application to be fault tolerant while keeping a small-size model and low complexity, making it suitable for embedded real-time classification.